We propose a novel approach for trip prediction by analyzing user's trip histories. We augment users' (self-) trip histories by adding “similar” trips from other users, which could be informative and useful for predicting future trips for a given user. This also helps to cope with noisy or sparse trip histories, where the self-history by itself does not provide a reliable prediction of future trips. We show empirical evidence that by enriching the users' trip histories with additional trips, one can improve the prediction error by 15%∼40%, evaluated on multiple subsets of the Nancy2012 dataset. This real-world dataset is collected from public transportation ticket validations in the city of Nancy, France. Our prediction tool is a central component of a trip simulator system designed to analyze the functionality of public transportation in the city of Nancy.


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    Title :

    Trip Prediction by Leveraging Trip Histories from Neighboring Users


    Contributors:


    Publication date :

    2022-10-08


    Size :

    711829 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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